Name
Title | ||
|---|---|---|
Integrating Optimal Simulation Budget Allocation and Genetic Algorithm to Find the Approximate Pareto Patient Flow Distribution |
Abstract | ||
|---|---|---|
The imbalanced development among different levels of healthcare facilities has become a major social issue in China’s urban healthcare system, which has raised the irrational patient flow distribution on the levels of both intra-hospital and inter-healthcare facilities. In this research, we develop a methodology to find the optimal macrolevel patient flow distribution in terms of multidimension inputs and outputs for the two-level healthcare system. The proposed method integrates the discrete-event simulation (DES), the multiobjective optimization and the simulation budget allocation together to comprehensively improve the overall system performances by finding the approximate Pareto patient flow distribution in the hierarchical healthcare system. The multiobjective optimal computing budget allocation (MOCBA) is applied to improve the efficiency, where the nondominated probability is functioned as the fitness measurement to each design. A case study based on the real data is carried out to validate and implement the proposed method. The results of the case study show the recommended Pareto optimal patient flow distribution can improve the overall hierarchical system performances and our methodology are qualified as a quantitative decision tool for decision makers. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TASE.2015.2424975 | Automation Science and Engineering, IEEE Transactions |
Keywords | DocType | Volume |
Resource management,Computational modeling,Pareto optimization,Hospitals,Probability | Journal | PP |
Issue | ISSN | Citations |
99 | 1545-5955 | 7 |
PageRank | References | Authors |
0.48 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jie Song | 1 | 34 | 4.14 |
| Yunzhe Qiu | 2 | 8 | 1.51 |
| Zekun Liu | 3 | 8 | 1.18 |